Method and system for bundling data insights

ABSTRACT

A system and a method for bundling data insights, in accordance with respective use cases are provided herein. The method may include the following steps: obtaining a plurality of data insights relating to data originated from a plurality of data sources; maintaining a use cases database, holding a plurality of use cases, each affiliated with a respective blend of said data insights; receiving one or more requests from clients for data insights, each request associated with respective business requirements; and providing the clients with a respective one of the bundles according to the respective business requirements and responsive to the requests and based on the use cases database.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of U.S. patent applicationSer. No. 16/848,251, filed on Apr. 14, 2020, which claims the benefit ofand priority to U.S. Provisional Patent Application No. 62/833,709,filed on Apr. 14, 2019, both are incorporated herein by reference intheir entirety and are co-owned by the Assignee of the instantapplication

FIELD OF THE INVENTION

The present invention relates generally to the field of data processingand, more particularly, to processing of automotive data and other typesof data over a computer network.

BACKGROUND OF THE INVENTION

Prior to setting forth the background of the invention, it may behelpful to provide herein definitions of certain terms that will be usedhereinafter.

The term “connected vehicle” as used herein is defined as a car or anyother motor vehicle, such as a drone or an aerial vehicle, that isequipped with any form of wireless network connectivity enabling it toprovide and collect data from the wireless network. The data originatedfrom and related to connected vehicles and their parts is referredherein to as “automotive data”.

The term “data marketplace” or “data market” as used herein is definedas an online computerized platform that enables a plurality of dataconsumers to access and consume data. Data marketplaces typically offervarious types of data for different markets and from different sources.Common types of data consumers of the automotive data marketplace mayinclude the following domains: financial and insurance institutions,entertainment and navigation applications, safety and emergency,demography, and research and many more.

The term “data insight” as used herein refers to a deeper understandingof an entity (humans or organizations) achieved by applying a set ofdata science algorithms and techniques which lead to deduction, andanalyzing of data, possibly from multiple sources in a specific context.

Data consumed in these marketplaces may be used by businesses of allkinds, fleets, business and safety applications, and many types ofanalysts. Data marketplaces have proliferated with the growth of bigdata, as the amount of data collected by municipalities and smartcities, businesses, websites, and services has increased, and all thatdata has become increasingly recognized as an asset.

One of the challenges in managing a data marketplace is to enablecustomer access to data sources and effectively use the data obtainedfrom the sensors they need. Another challenge is to enable customeraccess to data insights from the data sources they need, the challengesare both from a technical and a business point of view. It is usuallynot clear to data consumers (subscribers) what access to what sensors ordata insights they would need and in what format that data, or datainsight is needed in order to optimize their use of the data theyconsume, for their own purposes.

Another challenge is to manage the various consent requirementsaffiliated with various data sources and/or data insight generators, interms of sharing the data with potential data consumers.

Matching between data sources and sensors or data insight generators,with the data consumers may become a very difficult task, especially ifcarried out on a sensor-by-sensor approach or per data insight approach.Such a sensor-by-sensor approach or per data insight approach are alsodifficult to implement, as it is difficult to enforce policies,difficult to bill consumers per sensor or per data insight and difficultto address the variance in using same sensors or same data insightalgorithms for different use case at the data consumer side.

SUMMARY OF THE INVENTION

In order to address the challenges, it has been suggested by theinventors of the present application to introduce data insightsbundling. In accordance with some embodiments of the present invention,a data insights bundle is a list of attributes that form a subset ofdata insights in a predefined blend that is sufficient for serving as abasis for implementing a specified use case.

Some embodiments of the present invention relate to expanding the methodof bundling data features relating to connected vehicles, into abundling of data insights deduced from any type of data source, inaccordance with respective use cases. The method may include thefollowing steps: obtaining a plurality of data insights relating to dataoriginated from a plurality of data sources; maintaining a use casesdatabase, holding a plurality of use cases, each affiliated with arespective blend of said data insights; receiving one or more requestsfrom clients for data insights, each request associated with respectivebusiness requirements; and providing said clients with a respective oneof said bundles according to the respective business requirements andresponsive to the requests and based on the use cases database. Inaccordance with some embodiments of the present invention, theaforementioned steps may be implemented on a computer server, as part ofa client-server network.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1 is a block diagram illustrating non-limiting exemplaryarchitecture of a marketplace server that is capable of bundling of dataand data insights relating to data in accordance with some embodimentsof the present invention;

FIG. 2A is a high-level flowchart illustrating one method in accordancewith some embodiments of the present invention;

FIG. 2B is a high-level flowchart illustrating a second method inaccordance with some embodiments of the present invention; and

FIG. 3 is a block diagram illustrating non-limiting exemplaryarchitecture of a networked server that runs on one more computerprocessors in accordance with some embodiments of the present invention.

It will be appreciated that, for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, various aspects of the present inventionwill be described. For purposes of explanation, specific configurationsand details are set forth in order to provide a thorough understandingof the present invention. However, it will also be apparent to oneskilled in the art that the present invention may be practiced withoutthe specific details presented herein. Furthermore, well known featuresmay be omitted or simplified in order not to obscure the presentinvention.

Unless specifically stated otherwise, as is apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, or the like, refer to the action and/orprocesses of a computer or computing system, or similar electroniccomputing device, that manipulates and/or transforms data represented asphysical, such as electronic, quantities within the computing system'sregisters and/or memories into other data similarly represented asphysical quantities within the computing system's memories, registers orother such information storage, transmission or display devices.

FIG. 1 is a block diagram illustrating non-limiting exemplaryarchitecture of system 100 for bundling data features and data insightsrelating to data, in accordance with respective use cases. The systemmay include: a server 120 configured to obtain a plurality of datainsights 112 originated from a plurality of data sources 110. System 100may further include a use cases database 160, configured to hold aplurality of use cases, each affiliated with a respective blend of thedata insights.

System 100 may include a bundle manager 140 implemented by a computerprocessor 130 configured to store a plurality of data features bundles,wherein each bundle is a list of attributes which form a subset of datafeatures and/or data insights which are sufficient (in terms of datasufficiency) to implement one of the use cases, and to receive one ormore requests via a requests manager 170 from one or more clients 10 fordata features and/or data insights, each request being associated withone or more respective business requirements. The bundle manager 140provides the clients with a respective one of the bundles 144A-144Naccording to the one or more respective business requirements andresponsive to the requests and based on the use cases database 160.

According to some embodiments of the present invention, system 100 mayfurther include a bundles generator 250 configured to generate bundles144A-144N prior to receiving the requests.

According to some embodiments of the present invention, system 100 mayfurther include a bundles generator 150 configured to generate bundleson-the-fly based on predefined rules, responsive to receiving therequests.

According to some embodiments of the present invention, system 100 mayfurther include a bundles generator 150 configured to generate bundlesbased on market research.

According to some embodiments of the present invention, system 100 mayfurther include a bundles generator 150 configured to generate bundlesusing machine learning.

According to some embodiments of the present invention, bundles144A-144N may include and exhibit a respective blend of data featuresand/or blends of data insights, wherein the blend administers at leastone of: types, formats, accuracy, frequency, latency, and sufficiency ofthe data features.

Several non-limiting exemplary bundles are shown below with theirbundled attributes respectively:

-   -   A bundle for UBI-PAYD (User Based Insurance-Pay As You        Drive)-Attributes: VIN, Time, odometer.    -   A bundle for UBI-PAHYD (User Based Insurance-Pay As How You        Drive) Attributes: VIN, Time, odometer, speed, acceleration,        hard break, latitude, longitude.    -   A bundle for Emergency (Upon Accident) Attributes: VIN, Time,        latitude, longitude, speed.

The use of bundles in accordance with some embodiments of the presentinvention may facilitate the matching between available providers thatqualify the use case. It will also facilitate and simplify the consentseeking process as the consent shall be now given get per use case andnot per attribute within a use case.

In accordance with some embodiments of the present invention, eachbundle may have, by nature, its own quality and frequencycharacteristics. In addition, several similar bundles can be used tocover a more specific need. The scope of the bundle can be broadened ornarrowed per the needs of the client.

In accordance with some embodiments of the present invention, for abundle that supports Emergency Basic (Upon Accident), the attributes canbe: VIN, Time, latitude, longitude, speed. For a more comprehensivebundle of Emergency Detailed (Upon Accident), the attributes mayinclude: VIN, Time, latitude, longitude, speed, number of passengers,and airbags.

Additionally, each bundle may support different types of API indicatingwhether the API is “must” or “optional” per bundle. For example, TheAPIs may take the form of: REST—basic, Reports—optional, Pushnotification—optional, Insights—optional, and Pre-defined aggregationstables—Optional.

According to some embodiment of the present invention the bundling maytake the form of data bundling of multiple data sources and datafeatures. Table 1 below illustrates by way of example only, possiblybundling of data sources and data features:

TABLE 1 Data sources Data features Connected Car Speed Data HeadingBreaks Internal sensors (occupancy, seatbelts) Tire pressure Batterylevel Fuel level Tire pressure Mobile data Location data Surrounding IoTsignals around us Geo boundaries Maps data Boundaries of neighborhoods,cities, counties, states and countries Socio-demographic Statistics suchas age, income, etc. data Geo Location data Points of interest RoadsLocations and their purpose Electric Vehicle EV chargers’ locations (EV)charging Types infrastructure Activity of the chargers Electricity gridGTFS data Public transportation networks routes and timetable

According to some embodiments of the present invention the data insightsderived from associated data source with data features may be bundled asdata insights bundles, based on various use cases.

According to some embodiments of the present invention, in order tobetter implement the data insights bundling, machine learning models maybe employed. The use of machine learning is applied to the data tocreate events. These events could be place events (for example arrivalto a specific place A), and transit events (for example the movementfrom Place A to Place B).

On top of the machine learning models there are additional algorithmiclayers, that utilizes and other data sources and data features intomeaningful insights, for example: a place event with arrival to place A,is then further analyzed, for example as a significant place, forexample, a work place, or a shopping area, and the intention or purposeof visit could be assigned, for example, work or shopping. An estimateduration of visit could be assigned as well, and that could be veryimportant to electric vehicle (EV) charging operators so they could knowif people stay enough time in that specific location so they couldcharge their EV. A transit event is then further analyzed, for example,a specific travel from A to B, could be analyzed as commute from home towork.

According to some embodiments of the present invention, by way ofexample only, the following use cases may be used in the context of datainsights bundling:

Urban Intelligence

Targeted to city and transportation planners involved in planning,approval, and allocation of resources for projects of different mobilitymodes in their areas of operations. Including the generation ofup-to-date visibility into mobility patterns in specified areasincluding detailed origin and destination matrices, visitation rates,traffic flow and volume, to allow transportation and city planners todesign and plan networks based on hyperlocal multimodal mobility needs.Adding sociodemographic profiles and purpose of trip creates even morevalue for urban planners who have previously been limited with basictransit “counting” data.

Electric Vehicle (EV) Intelligence

Targeted to businesses such as charge point operators or energycompanies who are establishing or expanding EV charging infrastructure.

Including the predictions of demand. Helping EV charge point operatorsto pinpoint optimal locations for placing future charging stations.Data-driven EV planners gain access to accurate measurements of demandfor EV charging sites in each city and precise mapping of underservedzones across primary EV corridors.

Mobility as a Service (MaaS) Intelligence:

Targeted to service providers of different mobility modes such as micromobility (bikes/eBikes, eScooters) and ride sharing. Helping theproviders of mobility services match supply with up-to-date demand databy deploying stations in optimal locations based on predictiveperformance metrics, reallocating their resources, and rebalancing theirfleet to maximize ridership. This also help cities drive positive modalshift by increasing service accessibility of clear and adaptive mobilityservices.

FIG. 2A is a high-level flowchart illustrating non-limiting exemplarymethod in accordance with some embodiments of the present invention. Amethod for bundling data features relating to connected vehicles, inaccordance with respective use cases, is provided herein. Method 200Amay include the following steps: obtaining a plurality of data featuresrelating to connected vehicles originated from a plurality of datasources 210A; maintaining a use cases database, holding a plurality ofuse cases, each affiliated with a respective blend of the data features220A; obtaining a plurality of data features bundles, wherein eachbundle is a list of attributes that form a subset of data features thatis sufficient to implement one of the use cases 230A; receiving one ormore requests from clients for data features, each request beingassociated with respective business requirements 240A; and providing theclients with a respective one of the bundles according to the respectivebusiness requirements and responsive to the requests and based on theuse cases database 250A.

FIG. 2B is a high-level flowchart illustrating non-limiting exemplarymethod in accordance with some embodiments of the present invention. Amethod for bundling data insights relating to data, in accordance withrespective use cases, is provided herein. Method 200B may include thefollowing steps: obtaining a plurality of data insights relating to dataoriginated from a plurality of data sources 210B; maintaining a usecases database, holding a plurality of use cases, each affiliated with arespective blend of the data insights 220B; obtaining a plurality ofdata insights bundles, wherein each bundle is a list of attributes thatform a subset of data insights that is sufficient to implement one ofthe use cases 230B; receiving one or more requests from clients for datainsights, each request being associated with respective businessrequirements 240B; and providing the clients with a respective one ofthe bundles according to the respective business requirements andresponsive to the requests and based on the use cases database 250B.

It should be noted that methods 200A and 200B according to someembodiments of the present invention may be stored as instructions in acomputer readable medium to cause processors, such as central processingunits (CPU) to perform the method. Additionally, the method described inthe present disclosure can be stored as instructions in a non-transitorycomputer readable medium, such as storage devices which may include harddisk drives, solid state drives, flash memories, and the like.Additionally, non-transitory computer readable medium can be memoryunits.

FIG. 3 is a block diagram illustrating non-limiting exemplaryarchitecture of a networked server that runs on one more computerprocessors in accordance with some embodiments of the present invention.

In accordance with some embodiments of the present invention, anautomotive data marketplace that offers data consumers with automotivedata features bundling may be presented as a Computing device 300 whichcan be used with embodiments of the invention. Computing device 300 caninclude a controller or processor 305 that can be or include, forexample, one or more central processing unit processor(s) (CPU), one ormore Graphics Processing Unit(s) (GPU or GPGPU), a chip or any suitablecomputing or computational device, an operating system 315, a memory320, a storage 330, input devices 335 and output devices 340.

Operating system 315 can be or can include any code segment designedand/or configured to perform tasks involving coordination, scheduling,arbitration, supervising, controlling or otherwise managing operation ofcomputing device 300, for example, scheduling execution of programs.Memory 320 can be or can include, for example, a Random Access Memory(RAM), a read only memory (ROM), a Dynamic RAM (DRAM), a SynchronousDRAM (SD-RAM), a double data rate (DDR) memory chip, a Flash memory, avolatile memory, a non-volatile memory, a cache memory, a buffer, ashort term memory unit, a long term memory unit, or other suitablememory units or storage units. Memory 320 can be or can include aplurality of, possibly different memory units. Memory 320 can store forexample, instructions to carry out a method (e.g. code 325), and/or datasuch as user responses, interruptions, etc.

Executable code 325 can be any executable code, e.g., an application, aprogram, a process, task or script. Executable code 325 can be executedby controller 305 possibly under control of operating system 415. Forexample, executable code 325 can when executed cause masking ofpersonally identifiable information (PII), according to some embodimentsof the invention. In some embodiments, more than one computing device300 or components of device 300 can be used for multiple functionsdescribed herein. For the various modules and functions describedherein, one or more computing devices 300 or components of computingdevice 300 can be used. Devices that include components similar ordifferent to those included in computing device 300 can be used and canbe connected to a network and used as a system. One or more processor(s)305 can be configured to carry out embodiments of the invention by forexample executing software or code. Storage 330 can be or can include,for example, a hard disk drive, a Compact Disk (CD) drive, aCD-Recordable (CD-R) drive, a universal serial bus (USB) device or othersuitable removable and/or fixed storage unit. Data such as instructions,code, NN model data, parameters, etc. can be stored in a storage 330 andcan be loaded from storage 330 into a memory 320 where it can beprocessed by controller 305.

Input devices 335 can be or can include for example a mouse, a keyboard,a touch screen or pad or any suitable input device. It will berecognized that any suitable number of input devices can be operativelyconnected to computing device 300 as shown by block 335. Output devices340 can include one or more displays, speakers and/or any other suitableoutput devices. It will be recognized that any suitable number of outputdevices can be operatively connected to computing device 300 as shown byblock 340. Any applicable input/output (I/O) devices can be connected tocomputing device 300, for example, a wired or wireless network interfacecard (NIC), a modem, printer or facsimile machine, a universal serialbus (USB) device or external hard drive can be included in input devices335 and/or output devices 340.

Some embodiments of the invention can include one or more article(s)(e.g., memory 320 or storage 330) such as a computer or processornon-transitory readable medium, or a computer or processornon-transitory storage medium, such as for example a memory, a diskdrive, or a USB flash memory, encoding, including or storinginstructions, e.g., computer-executable instructions, which, whenexecuted by a processor or controller, carry out methods disclosedherein.

Although embodiments of the invention are not limited in this regard,discussions utilizing terms such as, for example, “processing”,“computing”, “calculating”, “determining”, “establishing”, “analyzing”,“checking”, or the like, can refer to operation(s) and/or process(es) ofa computer, a computing platform, a computing system, or otherelectronic computing device, that manipulates and/or transforms datarepresented as physical (e.g., electronic) quantities within thecomputer's registers and/or memories into other data similarlyrepresented as physical quantities within the computer's registersand/or memories or other information non-transitory storage medium thatcan store instructions to perform operations and/or processes.

Although embodiments of the invention are not limited in this regard,the terms “plurality” and “a plurality” as used herein can include, forexample, “multiple” or “two or more”. The terms “plurality” or “aplurality” can be used throughout the specification to describe two ormore components, devices, elements, units, parameters, or the like. Theterm set when used herein can include one or more items. Unlessexplicitly stated, the method embodiments described herein are notconstrained to a particular order or sequence. Additionally, some of thedescribed method embodiments or elements thereof can occur or beperformed simultaneously, at the same point in time, or concurrently.

A computer program can be written in any form of programming language,including compiled and/or interpreted languages, and the computerprogram can be deployed in any form, including as a stand-alone programor as a subroutine, element, and/or other unit suitable for use in acomputing environment. A computer program can be deployed to be executedon one computer or on multiple computers at one site.

Method steps can be performed by one or more programmable processorsexecuting a computer program to perform functions of the invention byoperating on input data and generating output. Method steps can also beperformed by an apparatus and can be implemented as special purposelogic circuitry. The circuitry can, for example, be a FPGA (fieldprogrammable gate array) and/or an ASIC (application-specific integratedcircuit). Modules, subroutines, and software agents can refer toportions of the computer program, the processor, the special circuitry,software, and/or hardware that implement that functionality.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read-only memory or arandom access memory or both. The essential elements of a computer are aprocessor for executing instructions and one or more memory devices forstoring instructions and data. Generally, a computer can be operativelycoupled to receive data from and/or transfer data to one or more massstorage devices for storing data (e.g., magnetic, magneto-optical disks,or optical disks).

Data transmission and instructions can also occur over a communicationsnetwork.

Information carriers suitable for embodying computer programinstructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices. Theinformation carriers can, for example, be EPROM, EEPROM, flash memorydevices, magnetic disks, internal hard disks, removable disks,magneto-optical disks, CD-ROM, and/or DVD-ROM disks. The processor andthe memory can be supplemented by, and/or incorporated in specialpurpose logic circuitry.

To provide for interaction with a user, the above described techniquescan be implemented on a computer having a display device, a transmittingdevice, and/or a computing device. The display device can be, forexample, a cathode ray tube (CRT) and/or a liquid crystal display (LCD)monitor. The interaction with a user can be, for example, a display ofinformation to the user and a keyboard and a pointing device (e.g., amouse or a trackball) by which the user can provide input to thecomputer (e.g., interact with a user interface element). Other kinds ofdevices can be used to provide for interaction with a user. Otherdevices can be, for example, feedback provided to the user in any formof sensory feedback (e.g., visual feedback, auditory feedback, ortactile feedback). Input from the user can be, for example, received inany form, including acoustic, speech, and/or tactile input.

The computing device can include, for example, a computer, a computerwith a browser device, a telephone, an IP phone, a mobile device (e.g.,cellular phone, personal digital assistant (PDA) device, laptopcomputer, electronic mail device), and/or other communication devices.The computing device can be, for example, one or more computer servers.The computer servers can be, for example, part of a server farm. Thebrowser device includes, for example, a computer (e.g., desktopcomputer, laptop computer, and tablet) with a World Wide Web browser(e.g., Microsoft® Internet Explorer® available from MicrosoftCorporation, Chrome available from Google, Mozilla® Firefox availablefrom Mozilla Corporation, Safari available from Apple). The mobilecomputing device includes, for example, a personal digital assistant(PDA).

Website and/or web pages can be provided, for example, through a network(e.g., Internet) using a web server. The web server can be, for example,a computer with a server module (e.g., Microsoft® Internet InformationServices available from Microsoft Corporation, Apache Web Serveravailable from Apache Software Foundation, Apache Tomcat Web Serveravailable from Apache Software Foundation).

The storage module can be, for example, a random-access memory (RAM)module, a read only memory (ROM) module, a computer hard drive, a memorycard (e.g., universal serial bus (USB) flash drive, a secure digital(SD) flash card), and/or any other data storage device. Informationstored on a storage module can be maintained, for example, in a database(e.g., relational database system, flat database system) and/or anyother logical information storage mechanism.

The above-described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributing computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The components ofthe system can be interconnected by any form or medium of digital datacommunication (e.g., a communication network). Examples of communicationnetworks include a local area network (LAN), a wide area network (WAN),the Internet, wired networks, and/or wireless networks.

The system can include clients and servers. A client and a server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

The above-described networks can be implemented in a packet-basednetwork, a circuit-based network, and/or a combination of a packet-basednetwork and a circuit-based network. Packet-based networks can include,for example, the Internet, a carrier internet protocol (IP) network(e.g., local area network (LAN), wide area network (WAN), campus areanetwork (CAN), metropolitan area network (MAN), home area network (HAN),a private IP network, an IP private branch exchange (IPBX), a wirelessnetwork (e.g., radio access network (RAN), 802.11 network, 802.16network, general packet radio service (GPRS) network, HiperLAN), and/orother packet-based networks. Circuit-based networks can include, forexample, the public switched telephone network (PSTN), a private branchexchange (PBX), a wireless network (e.g., RAN, Bluetooth®, code-divisionmultiple access (CDMA) network, time division multiple access (TDMA)network, global system for mobile communications (GSM) network), and/orother circuit-based networks.

Some embodiments of the present invention may be embodied in the form ofa system, a method or a computer program product. Similarly, someembodiments may be embodied as hardware, software or a combination ofboth. Some embodiments may be embodied as a computer program productsaved on one or more non-transitory computer readable medium (or media)in the form of computer readable program code embodied thereon. Suchnon-transitory computer readable medium may include instructions thatwhen executed cause a processor to execute method steps in accordancewith embodiments. In some embodiments, the instructions stored on thecomputer readable medium may be in the form of an installed applicationand in the form of an installation package.

Such instructions may be, for example, loaded by one or more processorsand get executed. For example, the computer readable medium may be anon-transitory computer readable storage medium. A non-transitorycomputer readable storage medium may be, for example, an electronic,optical, magnetic, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any combination thereof.

Computer program code may be written in any suitable programminglanguage. The program code may execute on a single computer system, oron a plurality of computer systems.

One skilled in the art will realize the invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are, therefore, to beconsidered in all respects illustrative rather than limiting of theinvention described herein. Scope of the invention is thus indicated bythe appended claims, rather than by the foregoing description, and allchanges that come within the meaning and range of equivalency of theclaims are therefore intended to be embraced therein.

In the foregoing detailed description, numerous specific details are setforth in order to provide an understanding of the invention. However, itwill be understood by those skilled in the art that the invention can bepracticed without these specific details. In other instances, well-knownmethods, procedures, and components, modules, units and/or circuits havenot been described in detail so as not to obscure the invention. Somefeatures or elements described with respect to one embodiment can becombined with features or elements described with respect to otherembodiments.

1. A method of bundling data insights, in accordance with respective usecases, the method comprising: obtaining a plurality of data insightsrelating to data originated from a plurality of data sources;maintaining a use cases database, holding a plurality of use cases, eachaffiliated with a respective blend of the data insights; generating aplurality of data insights bundles, wherein each bundle is a list ofattributes that form a subset of data insights which is sufficient toimplement one of said use cases; receiving one or more requests fromclients for data insights, each request associated with one or morerespective business requirements; and providing said clients with arespective one of said data insights bundles, according to therespective one or more business requirements and responsive to therequests and based on the use cases database.
 2. The method according toclaim 1, wherein said data insights bundles are pre-generated prior toreceiving the requests.
 3. The method according to claim 1, wherein saiddata insights bundles are generated on-the-fly based on predefinedrules, responsive to receiving the requests.
 4. The method according toclaim 1, wherein said data insights bundles are generated based onmarket research.
 5. The method according to claim 1, wherein said datainsights bundles are generated using machine learning.
 6. The methodaccording to claim 1, wherein said data insights bundles comprise arespective blend of data insights, wherein the blend administers atleast one of: types, formats, accuracy, frequency, latency, andsufficiency of the data insights.
 7. A system for bundling datainsights, in accordance with respective use cases, the systemcomprising: a server configured to obtain a plurality of data insightsrelating to data originated from a plurality of data sources; a usecases database, configured to hold a plurality of use cases, eachaffiliated with a respective blend of said data insights; and a bundlemanager implemented by a computer processor configured to: store aplurality of data insights bundles, wherein each bundle is a list ofattributes that form a subset of data insights that is sufficient toimplement one of said use cases; and receive one or more requests fromclients for data insights, each request associated with respectivebusiness requirements; and provide said clients with a respective one ofsaid bundles according to the respective business requirements andresponsive to the requests and based on the use cases database.
 8. Thesystem according to claim 7, further comprising a bundles generatorwherein said bundles generator is configured to generate said datainsights bundles prior to receiving the requests.
 9. The systemaccording to claim 7, further comprising a bundles generator whereinsaid bundles generator is configured to generate said data insightsbundles on-the-fly based on predefined rules, responsive to receivingthe requests.
 10. The system according to claim 7, further comprising abundles generator wherein said bundles generator is configured togenerate said data insights bundles based on market research.
 11. Themethod according to claim 7, further comprising a bundles generatorwherein said bundles generator is configured to generate said datainsights bundles using machine learning.
 12. The system according toclaim 7, wherein said data insights bundles comprise a respective blendof data insights, wherein the blend administers at least one of: types,formats, accuracy, frequency, latency, and sufficiency of the datainsights.
 13. A non-transitory computer readable storage medium forbundling data insights, in accordance with respective use cases, thecomputer readable storage medium comprising a set of instructions that,when executed, cause at least one computer processor to: obtain aplurality of data insights relating to data originated from a pluralityof data sources; hold a plurality of use cases, each affiliated with arespective blend of said data insights; store a plurality of datainsights bundles, wherein each bundle is a list of attributes that forma subset of data insights that is sufficient to implement one of saiduse cases; and receive one or more requests from clients for datainsights, each request associated with respective business requirements;and provide said clients with a respective one of said bundles accordingto the respective business requirements and responsive to the requestsand based on the use cases database.
 14. The non-transitory computerreadable storage medium according to claim 13, further comprising a setof instructions that, when executed, cause the at least one computerprocessor to generate said data insights bundles prior to receiving therequests.
 15. The non-transitory computer readable storage mediumaccording to claim 13, further comprising a set of instructions that,when executed, cause the at least one computer processor to generatesaid data insights bundles on-the-fly based on predefined rules,responsive to receiving the requests.
 16. The non-transitory computerreadable storage medium according to claim 13, further comprising a setof instructions that, when executed, cause the at least one computerprocessor to generate said data insights bundles based on marketresearch.
 17. The non-transitory computer readable storage mediumaccording to claim 13, further comprising a set of instructions that,when executed, cause the at least one computer processor to generatesaid data insights bundles using machine learning.
 18. Thenon-transitory computer readable storage medium according to claim 13,wherein said data insights bundles comprise a respective blend of datainsights, wherein the blend administers at least one of: types, formats,accuracy, frequency, latency, and sufficiency of the data insights.